
import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import ViTMAEForPreTraining, ViTMAEModel
    from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor




class 			UpperCAmelCase_     :


  '''simple docstring'''







  def __init__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					Tuple=13     ,				UpperCamelCase__     :					int=30     ,				UpperCamelCase__     :					Tuple=2     ,				UpperCamelCase__     :					Tuple=3     ,				UpperCamelCase__     :					Optional[int]=True     ,				UpperCamelCase__     :					Tuple=True     ,				UpperCamelCase__     :					List[str]=32     ,				UpperCamelCase__     :					Optional[int]=5     ,				UpperCamelCase__     :					int=4     ,				UpperCamelCase__     :					List[str]=37     ,				UpperCamelCase__     :					Optional[int]="gelu"     ,				UpperCamelCase__     :					int=0.1     ,				UpperCamelCase__     :					Optional[Any]=0.1     ,				UpperCamelCase__     :					Tuple=10     ,				UpperCamelCase__     :					Any=0.02     ,				UpperCamelCase__     :					Union[str, Any]=3     ,				UpperCamelCase__     :					Tuple=0.6     ,				UpperCamelCase__     :					List[Any]=None     ,				)     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			parent
    __magic_name__								=			batch_size
    __magic_name__								=			image_size
    __magic_name__								=			patch_size
    __magic_name__								=			num_channels
    __magic_name__								=			is_training
    __magic_name__								=			use_labels
    __magic_name__								=			hidden_size
    __magic_name__								=			num_hidden_layers
    __magic_name__								=			num_attention_heads
    __magic_name__								=			intermediate_size
    __magic_name__								=			hidden_act
    __magic_name__								=			hidden_dropout_prob
    __magic_name__								=			attention_probs_dropout_prob
    __magic_name__								=			type_sequence_label_size
    __magic_name__								=			initializer_range
    __magic_name__								=			mask_ratio
    __magic_name__								=			scope

    # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
    # (we add 1 for the [CLS] token)
    __magic_name__								=			(image_size // patch_size) ** 2
    __magic_name__								=			int(math.ceil((1 - mask_ratio) * (num_patches + 1)       )       )
  def 				_lowercase				(					self     :					Tuple       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

    __magic_name__								=			None
    if self.use_labels:
      __magic_name__								=			ids_tensor([self.batch_size]     ,				self.type_sequence_label_size       )

    __magic_name__								=			self.get_config()

    return config, pixel_values, labels
  def 				_lowercase				(					self     :					List[str]       )     ->      List[str]:







    """simple docstring"""






    return ViTMAEConfig(
        image_size=self.image_size     ,				patch_size=self.patch_size     ,				num_channels=self.num_channels     ,				hidden_size=self.hidden_size     ,				num_hidden_layers=self.num_hidden_layers     ,				num_attention_heads=self.num_attention_heads     ,				intermediate_size=self.intermediate_size     ,				hidden_act=self.hidden_act     ,				hidden_dropout_prob=self.hidden_dropout_prob     ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,				is_decoder=UpperCamelCase__     ,				initializer_range=self.initializer_range     ,				mask_ratio=self.mask_ratio     ,				)
  def 				_lowercase				(					self     :					int     ,				UpperCamelCase__     :					Optional[Any]     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					str       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			ViTMAEModel(config=UpperCamelCase__       )
    model.to(UpperCamelCase__       )
    model.eval()
    __magic_name__								=			model(UpperCamelCase__       )
    self.parent.assertEqual(result.last_hidden_state.shape     ,				(self.batch_size, self.seq_length, self.hidden_size)       )
  def 				_lowercase				(					self     :					Any     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					List[Any]       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			ViTMAEForPreTraining(UpperCamelCase__       )
    model.to(UpperCamelCase__       )
    model.eval()
    __magic_name__								=			model(UpperCamelCase__       )
    __magic_name__								=			(self.image_size // self.patch_size) ** 2
    __magic_name__								=			self.patch_size**2 * self.num_channels
    self.parent.assertEqual(result.logits.shape     ,				(self.batch_size, num_patches, expected_num_channels)       )

    # test greyscale images
    __magic_name__								=			1
    __magic_name__								=			ViTMAEForPreTraining(UpperCamelCase__       )
    model.to(UpperCamelCase__       )
    model.eval()
    __magic_name__								=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
    __magic_name__								=			model(UpperCamelCase__       )
    __magic_name__								=			self.patch_size**2
    self.parent.assertEqual(result.logits.shape     ,				(self.batch_size, num_patches, expected_num_channels)       )





  def 				_lowercase				(					self     :					str       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			self.prepare_config_and_inputs()
    __magic_name__	,       __magic_name__	,       __magic_name__								=			config_and_inputs
    __magic_name__								=			{"""pixel_values""": pixel_values}
    return config, inputs_dict







@require_torch
class 			UpperCAmelCase_     (					_A    ,			_A    ,			unittest.TestCase	):


  '''simple docstring'''







  a__       			=						(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
  a__       			=						{"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}

  a__       			=						False
  a__       			=						False
  a__       			=						False
  a__       			=						False
  def 				_lowercase				(					self     :					int       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			ViTMAEModelTester(self       )
    __magic_name__								=			ConfigTester(self     ,				config_class=UpperCamelCase__     ,				has_text_modality=UpperCamelCase__     ,				hidden_size=37       )
  def 				_lowercase				(					self     :					Union[str, Any]       )     ->      str:







    """simple docstring"""






    self.config_tester.run_common_tests()
  @unittest.skip(reason="""ViTMAE does not use inputs_embeds"""       )
  def 				_lowercase				(					self     :					Any       )     ->      Optional[int]:







    """simple docstring"""






    pass
  def 				_lowercase				(					self     :					str       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__	,       __magic_name__								=			self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      __magic_name__								=			model_class(UpperCamelCase__       )
      self.assertIsInstance(model.get_input_embeddings()     ,				(nn.Module)       )
      __magic_name__								=			model.get_output_embeddings()
      self.assertTrue(x is None or isinstance(UpperCamelCase__     ,				nn.Linear       )       )
  def 				_lowercase				(					self     :					int       )     ->      int:







    """simple docstring"""






    __magic_name__	,       __magic_name__								=			self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      __magic_name__								=			model_class(UpperCamelCase__       )
      __magic_name__								=			inspect.signature(model.forward       )
      # signature.parameters is an OrderedDict => so arg_names order is deterministic
      __magic_name__								=			[*signature.parameters.keys()]

      __magic_name__								=			["""pixel_values"""]
      self.assertListEqual(arg_names[:1]     ,				UpperCamelCase__       )
  def 				_lowercase				(					self     :					str       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCamelCase__       )
  def 				_lowercase				(					self     :					Any       )     ->      str:







    """simple docstring"""






    __magic_name__								=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_pretraining(*UpperCamelCase__       )
  def 				_lowercase				(					self     :					Optional[Any]     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					Optional[int]       )     ->      Union[str, Any]:







    """simple docstring"""






    np.random.seed(2       )

    __magic_name__								=			int((pt_model.config.image_size // pt_model.config.patch_size) ** 2       )
    __magic_name__								=			np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )
    __magic_name__								=			torch.from_numpy(UpperCamelCase__       )

    # Add `noise` argument.
    # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
    __magic_name__								=			pt_noise

    super().check_pt_tf_models(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       )
  def 				_lowercase				(					self     :					Any       )     ->      Any:







    """simple docstring"""






    __magic_name__	,       __magic_name__								=			self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      __magic_name__								=			model_class(UpperCamelCase__       )
      model.to(UpperCamelCase__       )
      model.eval()
      # make random mask reproducible
      torch.manual_seed(2       )
      with torch.no_grad():
        __magic_name__								=			model(**self._prepare_for_class(UpperCamelCase__     ,				UpperCamelCase__       )       )

      __magic_name__								=			outputs[0].cpu().numpy()
      __magic_name__								=			0

      with tempfile.TemporaryDirectory() as tmpdirname:
        model.save_pretrained(UpperCamelCase__       )
        __magic_name__								=			model_class.from_pretrained(UpperCamelCase__       )
        model.to(UpperCamelCase__       )
        # make random mask reproducible
        torch.manual_seed(2       )
        with torch.no_grad():
          __magic_name__								=			model(**self._prepare_for_class(UpperCamelCase__     ,				UpperCamelCase__       )       )

        # Make sure we don't have nans
        __magic_name__								=			after_outputs[0].cpu().numpy()
        __magic_name__								=			0
        __magic_name__								=			np.amax(np.abs(out_a - out_a       )       )
        self.assertLessEqual(UpperCamelCase__     ,				1E-5       )
  @unittest.skip(
      reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""       )
  def 				_lowercase				(					self     :					List[Any]       )     ->      List[Any]:







    """simple docstring"""






    pass
  @unittest.skip(
      reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""       )
  def 				_lowercase				(					self     :					str       )     ->      Optional[int]:







    """simple docstring"""






    pass
  @unittest.skip(
      reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""       )
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Dict:







    """simple docstring"""






    pass
  @unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""       )
  def 				_lowercase				(					self     :					str       )     ->      List[str]:







    """simple docstring"""






    pass
  @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""       )
  def 				_lowercase				(					self     :					str       )     ->      Optional[int]:







    """simple docstring"""






    pass





  @slow
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Optional[Any]:







    """simple docstring"""






    for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      __magic_name__								=			ViTMAEModel.from_pretrained(UpperCamelCase__       )
      self.assertIsNotNone(UpperCamelCase__       )







def a__       (			):






  '''simple docstring'''





  __magic_name__								=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
  return image





@require_torch
@require_vision
class 			UpperCAmelCase_     (					unittest.TestCase	):


  '''simple docstring'''







  @cached_property
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      List[Any]:







    """simple docstring"""






    return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""       ) if is_vision_available() else None





  @slow
  def 				_lowercase				(					self     :					Tuple       )     ->      int:







    """simple docstring"""






    np.random.seed(2       )

    __magic_name__								=			ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""       ).to(UpperCamelCase__       )

    __magic_name__								=			self.default_image_processor
    __magic_name__								=			prepare_img()
    __magic_name__								=			image_processor(images=UpperCamelCase__     ,				return_tensors="""pt"""       ).to(UpperCamelCase__       )

    # prepare a noise vector that will be also used for testing the TF model
    # (this way we can ensure that the PT and TF models operate on the same inputs)
    __magic_name__								=			ViTMAEConfig()
    __magic_name__								=			int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2       )
    __magic_name__								=			np.random.uniform(size=(1, num_patches)       )

    # forward pass
    with torch.no_grad():
      __magic_name__								=			model(**UpperCamelCase__     ,				noise=torch.from_numpy(UpperCamelCase__       ).to(device=UpperCamelCase__       )       )

    # verify the logits
    __magic_name__								=			torch.Size((1, 196, 768)       )
    self.assertEqual(outputs.logits.shape     ,				UpperCamelCase__       )

    __magic_name__								=			torch.tensor(
        [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]       )

    self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]     ,				expected_slice.to(UpperCamelCase__       )     ,				atol=1E-4       )       )



def a__       (			A_=28123   ):






  '''simple docstring'''





  __magic_name__								=			[1] * (limit + 1)

  for i in range(2,	int(limit**0.5   ) + 1   ):
    sum_divs[i * i] += i
    for k in range(i + 1,	limit // i + 1   ):
      sum_divs[k * i] += k + i

  __magic_name__								=			set()
  __magic_name__								=			0

  for n in range(1,	limit + 1   ):
    if sum_divs[n] > n:
      abundants.add(A_   )

    if not any((n - a in abundants) for a in abundants   ):
      res += n

  return res


if __name__ == "__main__":
    print(solution())

